Rebate Analysis AI Agent

About the Agent

The Rebate Analysis AI Agent automates rebate validation and calculation, ensuring precise, efficient, and error-free processing of rebate claims. By integrating with contract management systems and leveraging invoice data, it cross-references invoices against contract terms to verify eligibility, accurately calculates applicable rebates, and generates structured, actionable reports. This automation minimizes manual errors, accelerates processing times, and enhances financial accuracy, ultimately driving compliance, cost savings, and operational efficiency.

Challenges That the Rebate Analysis AI Agent Addresses

Manual rebate analysis involves tedious invoice verification, contract clause cross-referencing, and rebate calculations, often leading to financial discrepancies, delays, and compliance risks. Finance teams struggle with tracking rebates accurately, resulting in missed opportunities, inconsistencies, and an increased administrative workload.

The Rebate Analysis AI Agent overcomes these challenges by automating rebate validation, ensuring accurate calculations, and optimizing financial workflows. By reducing manual effort and accelerating processing times, it enhances financial transparency, improves rebate recovery, maximizes utilization, and boosts overall financial efficiency.

How the Agent Works

The ZBrain Rebate Calculation Agent automates and streamlines the rebate processing workflow, ensuring accuracy and efficiency. The agent is triggered when a new Proof of Delivery (POD) email arrives in a designated inbox, initiating a series of automated steps. Leveraging a Large Language Model (LLM), it analyzes incoming data, cross-references contract details, and calculates rebates in real time. Below is a step-by-step breakdown of the process:

Step 1: Proof of Delivery (POD) Detection and Data Extraction

The agent scans incoming emails to detect and process Proof of Delivery (POD) documents, extracting key details to initiate rebate calculations.

Key Tasks:

• POD Identification: Determines whether the email contains a valid POD in the body text or as an attachment (PDF, Word, scanned document, etc.).
• Attachment Processing: If a POD is attached, the agent utilizes an LLM to analyze the document.
• Data Extraction: Extracts essential information such as invoice number, PO number, tracking number, delivery date, SKU, and product details.

Outcome: The agent successfully extracts all necessary data from the POD, making it available for further processing.

Step 2: Invoice Matching Using the Knowledge Base (KB)

After extracting the invoice number, the agent searches the knowledge base (KB) to match it with an existing invoice, ensuring accurate rebate processing.

Key Tasks:

• Invoice Lookup: Queries the KB using the extracted invoice number to locate corresponding invoice records.
• Data Cross-Verification: Compares extracted invoice details (PO number, tracking number, delivery date, SKU) with KB records for consistency and validation.

Outcome: The correct invoice is identified, ensuring data integrity for rebate calculations.

Step 3: SKU Retrieval & Contract Metadata Verification

The agent cross-references SKU and product details from the verified invoice against a contract metadata repository, ensuring compliance with rebate terms.

Key Tasks:

• SKU Extraction: Retrieves SKU and product details from the invoice.
• Contract Validation: Matches extracted details against contract metadata (vendor contracts, product specifics, logistics partners, rebate terms).
• Rebate Eligibility Check: Determines if the transaction qualifies for a rebate based on contract conditions.

Outcome: If the transaction is eligible for a rebate, the process moves to Step 4. If not, the agent generates an appropriate response.

Step 4: Rebate Validation & Calculation

For eligible transactions, the agent retrieves the relevant contract, validates its terms, and computes the rebate amount based on predefined rules.

Key Tasks:

• Contract Retrieval: Fetches the applicable contract from the KB for reference.
• Validation: Checks contract details, including effective dates, rebate percentages, tiered structures, and special conditions.
• Rebate Calculation: Computes the rebate amount using contracted rates, product quantity, and predefined formulas.
• Rebate Summary Generation: Records delivery date, SKU, quantity, logistics partner, rebate per unit, and total rebate amount in the final rebate summary sheet.
• Stakeholder Notification: Generates an automated notification email through the associated system to inform relevant teams about the rebate calculation results, ensuring transparency and timely action.

Outcome: The rebate is accurately calculated, recorded, and communicated to stakeholders for transparency.

Step 5: Continuous Learning and Improvement

To ensure continuous improvement, the system integrates a human-in-the-loop feedback mechanism, allowing users to review processed rebates and optimize future calculations.

Key Tasks:

• Feedback Collection: Gathers user insights on rebate accuracy, flagging discrepancies or refinements.
• Performance Analysis: Identifies recurring issues and areas for improvement based on user feedback.
• Process Enhancement: Refines LLM models, data extraction accuracy, and contract matching logic based on human feedback.
• Performance Optimization: Continuously improves rebate processing through adaptive learning mechanisms.

Outcome: The agent continuously improves, becoming more accurate and adaptable to evolving business requirements.

Why Use the Rebate Analysis AI Agent?

• Automated Rebate Calculation: Fully automates the rebate calculation process, minimizing manual effort and errors while ensuring precise and timely rebate application.
• Faster Processing: Accelerates the rebate cycle by automating data extraction and calculations, reducing delays and improving cash flow.
• Operational Efficiency: Streamlines workflows by automating repetitive tasks, freeing employees to focus on higher-value activities and strategic initiatives.
• LLM-Powered Precision: Leverages large language models (LLMs) for accurate data extraction, matching, and rebate application, minimizing errors and maximizing rebate claims.
• Continuous Improvement: Integrates a human feedback loop, allowing the AI agent to refine its accuracy over time and adapt to evolving contract terms and rebate structures.
• Seamless Contract Integration:Connects with contract management systems for real-time access to contract terms, ensuring accurate rebate validation.